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In this study, the performance of generalized low-density parity-check (GLDPC) codes under the a posteriori probability (APP) decoder is analyzed. We explore the concentration, symmetry, and monotonicity properties of GLDPC codes under the APP decoder, extending the applicability of density evolution to GLDPC codes. We demonstrate that with an appropriate proportion of generalized constraint (GC) nodes, GLDPC codes can reduce the original gap to capacity compared to their original LDPC counterparts over the BEC and BI-AWGN channels. Additionally, on the BI-AWGN channel, we adopt Gaussian mixture distributions to approximate the message distributions from variable nodes and Gaussian distributions for those from constraint nodes. This approximation technique significantly enhances the precision of the channel parameter threshold compared to traditional Gaussian approximations while maintaining a low computational complexity similar to that of Gaussian approximations. Our simulation experiments provide empirical evidence that GLDPC codes, when decoded with the APP decoder and equipped with the right fraction of GC nodes, can achieve substantial performance improvements compared to low-density parity-check (LDPC) codes.
Chang et al. (Mon,) studied this question.